Summary of Information Guided Regularization For Fine-tuning Language Models, by Mandar Sharma et al.
Information Guided Regularization for Fine-tuning Language Models
by Mandar Sharma, Nikhil Muralidhar, Shengzhe Xu, Raquib Bin Yousuf, Naren Ramakrishnan
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In a departure from the traditional pretraining-fine-tuning paradigm, this study explores novel approaches to regularization for improved transfer learning in language models. By examining how task-sensitive parameters affect the pretraining loss landscape through an information-theoretic lens, researchers develop a new method called guided dropout that enhances model regularization and downstream generalization. This agnostic approach adds no computational overhead to fine-tuning and demonstrates superior performance even with limited data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study looks at ways to improve how language models learn from one task and apply it to another. Researchers found that some parts of the model are more important for transfer learning than others, so they created a new way to “prune” these parts to make the model better at generalizing to new tasks. This approach is helpful in situations where there’s not much data available. |
Keywords
» Artificial intelligence » Dropout » Fine tuning » Generalization » Pretraining » Regularization » Transfer learning
